Diseases such as cardiovascular disease (CVD) affect millions of patients in the United States. Proper clinical management of these diseases can be challenging and costly due to the various therapies and procedures required to provide effective disease treatment. For example, for heart failure (HF), proper clinical management of patients requires regular follow-up, expensive palliative therapies and, potentially, heart transplant. Additionally, for some diseases, treatments are not available. In these cases, clinical practice can only control the progression of the disease through medication or implantable devices. It is therefore crucial to identify the onset of the disease as early as possible to minimize its effect, and predict the progression of the disease in order to anticipate changes in lifestyle and treatments.
Early diagnosis and effective treatment planning and monitoring of a disease requires clinicians to gather data from various sources such as medical imaging, signal measurement, and molecular evaluation, to evaluate the disease as comprehensively as possible. While each of these techniques provides valuable information, each one of them, when taken individually, has its drawbacks. For example, molecular evaluation (e.g., based on blood samples or biopsies) may provide biomarkers of disrupted molecular pathways suggesting, for instance, the onset of a myocardial scar, fibrosis, or HF. However, the presence of abnormal biomarkers in laboratory tests does not provide any information regarding the location or dimension of the lesion. Conversely, imaging allows a clinician to quantify spatial and dynamic changes in heart morphology, structure and function. However, early stages of HF may not present visible symptoms like diminution of ejection fraction or presence of myocardium lesions, even if in vitro diagnostic (IVD) blood tests identified abnormal biomarkers.
Currently, clinicians perform and analyze imaging, signal, and molecular measurements independently, and then fuse the independent findings together at the end of the decision process, based on the clinician's experience or population-based guidelines. Data is not analyzed in a systematic and inter-dependent way. In some cases, data analysis is performed by different persons. This may result in sub-optimal diagnosis (with potentially contradictory conclusions), unnecessary monitoring, and not adapted therapies. Therefore, there is a need for a system that would integrate data items such as imaging, signal, and molecular data into a common framework for their joint analysis and interpretation.